Forecasting or making a prediction is an attempt to go beyond the current time horizon to provide an assertion about the future. Predicting an outcome that occurs in the future of an event is probabilistic in nature. In other words, a prediction that an event will occur, or not occur, is only indicative of a probability of the occurrence or non-occurrence of that event. The closer that probability is to 1 or one hundred percent, the more likely that event is to occur or not occur. However, whether that event actually occurs or does not occur cannot be known with certainly until the time of occurrence has passed.
Regression is a probabilistic classification model that attempts to fit probabilistic values to a graph plot, such as a line. The model plots a predictor variable and a dependent variable on the graph, and uses the graph to predict an outcome or a value of the dependent variable based on projected values of the predictor variable. An error is a value by which a forecasted outcome of a model differs from the actual outcome of the event being forecasted. A percentage error is a percentage value by which a forecasted outcome of a model differs from the actual outcome of the event being forecasted. Mean percentage error is a computed average of several measurements of a percentage error. Mean squared error is a computed average of the square of several error value measurements.
A model for predicting an outcome is called a prediction model. A prediction model (model) includes computational logic to compute the predicted outcome based on a set of inputs. Such logic includes rules, heuristics, statistical and other analytical algorithms, or a combination thereof.
A variety of prediction models are presently in use to make various types of predictions. For example, cyclical model predicts an outcome of an event based on the past outcomes of the same event that have a seasonal or periodic pattern. The cyclical model factors in the circumstances under which the event occurred in the past and the circumstances under which the event is expected to occur during the prediction period to make the prediction.
Simulation model is another example model for making predictions. The simulation model simulates in the present time the conditions under which the event will transpire in the future to reach an outcome in the present time. The outcome reached in the present time under simulated conditions forms the prediction for the event at the future time.
Extrapolation model draws educated conclusions about future conditions of the future event based on presently available information about the conditions. The extrapolation model then bases the prediction on those conclusions. Many other types of prediction models are presently in use.